Methods for noninvasively measuring analyte levels in a subject

ABSTRACT

A method for noninvasively measuring analyte levels includes using a non-imaging OCT-based system to scan a two-dimensional area of biological tissue and gather data continuously during the scanning. Structures within the tissue where measured-analyte-induced changes to the OCT data dominate over changes induced by other analytes are identified by focusing on highly localized regions of the data curve produced from the OCT scan which correspond to discontinuities in the OCT data curve. The data from these localized regions then can be related to measured analyte levels.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.11/445,631 entitled “Methods for Noninvasively Measuring Analyte Levelsin a Subject” filed Jun. 2, 2006, which claims the benefit of U.S.Provisional Application No. 60/686,721 entitled “Method forNoninvasively Measuring Blood Glucose” filed Jun. 2, 2005, the entirecontents of which are hereby incorporated herein by reference, and is acontinuation-in-part of U.S. application Ser. No. 10/916,236, entitled“Method And Apparatus For Monitoring Glucose Levels In A BiologicalTissue,” filed Aug. 11, 2004, the entire contents of which are herebyincorporated herein by reference. This application is also related toU.S. Provisional Application No. 60/671,285, entitled “Method For DataReduction And Calibration Of An OCT-Based Blood Glucose Monitor,” filedApr. 14, 2005, the entire contents of which are herein incorporated byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to methods for noninvasivelymeasuring blood glucose or other analyte levels in a subject bymeasuring localized changes in light scattering from skin or otherbiological tissue. For example, such a method can include identifyingtissue structures where the effect of blood glucose concentrations orlevels are high, and targeting localized regions within the identifiedstructures to measure blood glucose concentrations.

2. Description of the Related Art

Monitoring of blood glucose (blood sugar) levels has long been criticalto the treatment of diabetes in humans. Current blood glucose monitorsemploy a chemical reaction between blood serum and a test strip,requiring an invasive extraction of blood via a lancet or pinprick tothe finger. Although small handheld monitors have been developed toenable a patient to perform this procedure anywhere, at any time, theinconvenience associated with this procedure—specifically the bloodextraction and the need for test strips—has led to a low level ofcompliance by diabetic patients. Such low compliance can lead todiabetic complications. Thus, a non-invasive method for monitoring bloodglucose is needed.

Studies have shown that optical methods can be used to detect smallchanges in light scattering from biological tissue related to changes inlevels of blood sugar. Although highly complex, a first orderapproximation of transmitting monochromatic light through biologicaltissue can be described by the following simplified equation:I _(R) =I ₀ exp[−(μ_(a)+μ_(s))L],where I_(R) is the intensity of light reflected from the skin, I₀ is theintensity of the light illuminating the skin, μ_(a) is the absorptioncoefficient of the skin at the specific wavelength of the light, μ_(s)is the scattering coefficient of the skin at the specific wavelength ofthe light, and L is the total path traversed by the light. From thisrelationship, it can be seen that the intensity of the reflected lightdecays exponentially as either the absorption or the scattering by thetissue increases. The attenuation of light can be characterized by anattenuation coefficient, which is the sum of μ_(s) and μ_(a).

It is well established that there is a difference in the index ofrefraction between blood serum/interstitial fluid (IF) and cellmembranes (such as membranes of blood cells and skin cells). (See, R. C.Weast, ed., CRC Handbook of Chemistry and Physics, 70th ed., (CRCCleveland, Ohio 1989).) This difference can produce characteristicscattering of transmitted light. Glucose, in its varying forms, is amajor constituent of blood and IF. The variation in glucose levels ineither blood or IF changes the refractive index of blood-perfusedtissue, and thus the characteristic of scattering from such tissue.Further, glucose-induced changes to the refractive index aresubstantially greater than changes induced by variation ofconcentrations of other osmolytes in physiologically relevant ranges. Inthe near-infrared (NIR) wavelength range, blood glucose changes thescattering coefficient, μ_(s), more than it changes the absorptioncoefficient, μ_(a). Thus, optical scattering of the blood/IF and cellcombination varies as the blood glucose level changes. Accordingly,there is the potential for non-invasive measurement of blood glucoselevels.

Current non-invasive optical techniques being explored for blood glucoseapplications include polarimetry, Raman spectroscopy, near-infraredabsorption, scattering spectroscopy, photoacoustics, and optoacoustics.Despite significant efforts, these techniques have shortcomings, such aslow sensitivity (signal-to-noise ratio) for the glucose concentrationsat clinically-relevant levels, low accuracy (less than that of currentinvasive home monitors), and insufficient specificity of glucose levelmeasurement within a relevant physiological range of 1.7-27.8 mM/L or30-500 (mg/dL). For example, diffuse reflectance, or diffuse scattering,has been explored as a technique for noninvasively measuring levels ofblood glucose. M. Kohl, Optics Letters, 19(24) 2170-72 (1994); J. S.Maier, et al., Optics Letters, 19(24) 2062-64 (1994). Using diffusereflectance, a glucose-induced change of around 0.2%-0.3% in thescattering coefficient per 18 mg/dL (or 1 mM/L) has been measured. Thismeasured change is too small to be utilized efficiently for ablood-glucose monitor for home use. Additionally, glucose-inducedchanges to the scattering coefficient can be masked by changes inducedby temperature, hydration, and/or other osmolytes. Accordingly, there isa need for a method to conveniently, accurately, and non-invasivelymonitor glucose levels in blood.

Optical coherence tomography, or OCT, is an optical imaging techniquethat uses light waves to produce high-resolution imagery of biologicaltissue. OCT produces images by interferometrically scanning, in depth, alinear succession of spots and measuring absorption and/or scattering atdifferent depths at each successive spot. The data then is processed topresent an image of the linear cross section. The key benefits of such asystem in imaging applications include the ability to achieve a highresolution, e.g., better than 10 micrometers, and the ability to selectthe depth at which a sample can be imaged. For example, blood vesselsbeneath the surface of the skin can be imaged using such a system.

As discussed in U.S. application Ser. No. 10/916,236, and in R. O.Esenaliev, et al., Optics Letters, 26(13) 992-94 (2001), the entiredisclosure of which is incorporated by reference, it has been proposedthat OCT might be useful in measuring blood glucose. However,difficulties associated with this technique include the large number ofscans required to reduce optical noise, or speckle, which arises fromwavefront distortion when coherent light scatters from tissue. While anOCT imaging system can reduce speckle by averaging it out over manyscans or measurements, this approach is time-consuming, which makes theuse of a conventional OCT imaging system impractical for in-homemonitoring of blood glucose levels. Additionally, an OCT imaging systemrequires complex processing to form a workable image and to analyze theimage data sufficiently in order to determine glucose levels.

Accordingly, there is a need for enhanced OCT systems for measuringanalytes such as blood glucose levels.

SUMMARY OF THE INVENTION

In accordance with the present invention, a method for non-invasivelymeasuring glucose levels in blood is presented. Specifically, changes ina scattering profile produced from an OCT-based monitor are related tochanges in blood glucose levels by focusing on highly localized regionsof the scattering profile where changes to the scattering profileinduced by temperature, hydration, and other osmolytes are negligible.Glucose-induced changes to the scattering coefficient measured fromthese localized regions range between about 2% and about 20% per 1 mM/Lor 18 mg/dL, with an average value of about 12% per 18 mg/dL. Thesepercentage values are significantly higher than those measured usingother methods. Additionally, within the localized regions, effects tothe scattering coefficient induced by temperature, hydration, and otherosmolytes are negligible compared to the effects of glucose, and,accordingly, can be ignored. The changes in the scattering profile canbe related to changes in glucose concentrations by one or moremathematical algorithms.

A method for noninvasively measuring blood glucose-levels in biologicaltissue is described herein. The method includes the steps of scanning atwo-dimensional area of skin with a monitor based on non-imaging opticalcoherence tomography, collecting cross-sectional depth measurement datacontinuously during the scanning step, and identifying at least onelocalized region within the cross-sectional depth measurement data,wherein the at least one localized region corresponds to a structurewithin the skin where glucose-induced changes to the cross-sectionaldepth measurement data are prominent. Further, the method includes thestep of relating the cross-sectional depth measurement data to bloodglucose levels.

In one exemplary embodiment, a method for calibrating OCT measurementsusing multiple light wavelengths is described to identify a tissue formeasurement. At least two OCT scattering profiles can be obtained fromlight attenuated by a subject's tissue as a function of tissue depth.Non-limiting types of tissue include vascular tissue (e.g., a bloodvessel wall), at least one component of blood, dermal tissue surroundingthe vascular tissue, or some combination of the aforementioned types.The OCT scattering profiles can be obtained at different wavelengths oflight such that the tissue can exhibit a different attenuationcoefficient for each wavelength. The attenuation of light can be basedat least in part on the presence of an analyte associated with thetissue (e.g., water or hemoglobin). The wavelengths can also be chosensuch that the tissue has a different absorption coefficient at the twowavelengths. The wavelengths can also be chosen such that the scatteringcoefficient is larger than the absorption coefficient at the firstselected wavelength, and optionally the absorption coefficient at thesecond wavelength is larger than the absorption coefficient at the firstwavelength. A localized region (e.g., one or more depths) can beidentified corresponding to a tissue location of OCT measurementcalibration. Such calibration can be based upon a differentialcomparison of the two OCT scattering profiles. A blood glucosemeasurement (e.g., some type of chemical blood analysis measurement) canbe associated with each of the OCT scattering profiles for calibratingother OCT measurements (e.g., using the OCT scattering profiles andblood glucose measurements to make a calibration between attenuationcoefficient and blood glucose concentration). In general, the localizedregion can have changing light attenuation coefficients based on thepresence of blood glucose or other measurable analytes.

With respect to the exemplary method previously described, the OCTscattering profiles can be normalized prior to differential comparison,with a depth corresponding to a tissue location for OCT measurementcalibration depending upon a differential comparison of normalized OCTprofiles (e.g., subtracting one normalized profile from another atcorresponding depth locations). Normalization can be performed bydividing the scattering data of a respective OCT profile by theprofile's respective peak intensity value. One or more extrema points inthe differential comparison of normalized OCT profiles can beidentified, and subsequently correlated with the depth of the tissuelocation or some other measure of the localized region correspondingwith the tissue location.

In general, an offset location and an interval can define a localizedregion of an OCT scattering profile that can be correlated with aparticular attenuation coefficient. The offset can correspond with adepth of a tissue location, and the interval can be determined from theoffset location and the OCT scattering profile. The offset location andinterval can be used to define the region of the OCT scattering profilein which a slope measurement can be correlated with the attenuationcoefficient (or the scattering coefficient when absorption effects aresmall).

Another exemplary embodiment is directed to a method of determining anabsorption coefficient in OCT measurements using multiple lightwavelengths. Two or more OCT scattering profiles can be obtained as afunction of subject tissue depth at different wavelengths of light suchthat the tissue has a larger scattering coefficient than absorptioncoefficient at a first selected wavelength (e.g., the scatteringcoefficient being at least about 5 times greater than the absorptioncoefficient). A scattering coefficient can be determined from the firstOCT scattering profile (e.g., by locating a slope in the first OCTscattering profile). An estimate of a scattering coefficient from thesecond OCT scattering profile can be obtained from the scatteringcoefficient of the first OCT scattering profile. Such an estimate can beobtained using scattering theory (e.g., Mie scattering). The absorptioncoefficient of the second OCT scattering profile can be determined usingthe estimate of the scattering coefficient at the second selectedwavelength. A similar method can also be used to determine a scatteringcoefficient.

Another method consistent with an embodiment of the invention isdirected to calibrating OCT measurements using multiple lightwavelengths. Two or more OCT measurements can be obtained as a functionof time using different wavelengths of light for each measurement. Thewavelengths can be chosen such that the tissue has a larger absorptioncoefficient at a first selected wavelength relative to a second. Such anabsorption coefficient can also depend upon the presence of an analyte(e.g., water) in or around the tissue. One wavelength can also be chosensuch that the scattering coefficient exceeds the absorption coefficientby at least about a factor of five. A first OCT measurement can beconverted into an analyte measurement as a function of time. The analytemeasurement can be used to calibrate a scattering coefficientmeasurement as a function of time.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be more readily understood from the detaileddescription of the embodiment(s) presented below considered inconjunction with the attached drawings, of which:

FIG. 1 illustrates a process flow of a method for measuring bloodglucose;

FIG. 2 is a graphical illustration of a typical scattering cross sectionfrom a patch of human skin measured using an OCT-based monitor;

FIG. 3 is an example of an intensity difference plot, according to anembodiment of the present invention;

FIGS. 4A and 4B are graphical illustrations in which scatteringdiscontinuities are identified according to an embodiment of the presentinvention; and

FIG. 5 is a graphical illustration of an absorption effect of water atmultiple wavelengths, according to an embodiment of the presentinvention;

FIGS. 6A and 6B are examples of scattering profiles at wavelengths of1310 nanometers and 1440 nanometers, respectively, according to anembodiment of the present invention; and

FIG. 7 is a graphical illustration of a differential data set scatteringprofile, according to an embodiment of the present invention.

FIG. 8 is a schematic diagram illustrating a method of blood glucosemonitoring in accordance with the disclosure; and

FIG. 9 schematically illustrates an apparatus useful for practicing themethod of FIG. 8.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

According to an embodiment of the present invention, a method formeasuring blood glucose levels includes the step of utilizing anOCT-based sensor to take scattering cross-sectional depth measurementson a small area of biological tissue or skin. The OCT-based sensor canbe a non-imaging system such as that described in detail in U.S.application Ser. No. 10/916,236. In some embodiments, a two-dimensionalarea of the skin can be scanned, preferably either in a circularpattern, e.g., with a radius no greater than about 2 mm, or in a filleddisk or filled rectangular pattern where the pattern is drawn randomly.As the OCT-based sensor scans the two-dimensional pattern continuously,the sensor continuously collects data corresponding to cross-sectionaldepth measurements within the biological tissue. Other embodiments canutilize an OCT-based sensor to obtain cross-sectional depth measurementswith two-dimensional scanning.

By continuously acquiring a two-dimensional scan pattern andcontinuously taking cross-sectional depth measurements, the noiseassociated with OCT sensing, often referred to as “speckle,” is reducedmore efficiently than scanning tissue using a step-scan method, asdescribed in A. Kholodnykh, et al., Applied Optics, 42(16) 3027-37(2003). In Kholodnykh, a method proposed for an OCT-based systemincludes scanning a two-dimensional pattern using a step-scan process,where the OCT-based system light beam picks a spot on the skin and takesmultiple depth scans. The OCT-based system then averages these depthscans to reduce speckle, and moves on to another spot on the skin, takesmultiple depth scans, and averages the depth scans. The OCT-based systemrepeats this process until a two-dimensional pattern has been made.

In accordance with an embodiment of the present invention, the OCT-basedmonitor continuously scans an area of skin and continuously collectsdata. Using this method, fewer scans in less time are required toproduce sufficient results. To further reduce speckle, a number of OCTscans can be averaged to produce an average OCT scan result. Thus, dataassociated with a particular OCT scan at a specific point in time isactually an averaged result of a group of OCT scans.

Using the cross-sectional depth measurements, an intensity profile, orscattering profile, can be generated. Within the scattering profile,localized regions where changes to the scattering profile are dominatedby changes in blood glucose can be identified. To locate these regions,a second-derivative plot can be generated, as disclosed in U.S.Provisional Application No. 60/671,285. Using the second-derivativeplot, discontinuities in the scattering profile are exaggerated andeasily visualized. These discontinuities represent structures in theskin where changes in blood glucose levels dominate the scatteringprofile. Within these highly localized regions, changes to thescattering profile induced by temperature, hydration, and otherosmolytes, such as sodium, potassium, and urea, are very small comparedto the effects of glucose, and therefore, can be ignored.

By focusing on the localized regions identified in the intensityprofile, there are multiple means that can be used to correlateefficiently the scattering profile to blood glucose levels. Uponidentifying these localized regions, the data of the scattering profilecan be related to blood glucose levels using one or more mathematicalalgorithms such as, for example, an algorithm relating the slope of aportion of the OCT data curve to blood glucose levels, where the portionof the OCT data curve corresponds to a discontinuity in the scatteringprofile. An example of such an algorithm is disclosed in detail in U.S.Provisional Application No. 60/671,285. Optionally, the scatteringprofile can be related to blood glucose levels by utilizing a magnitudeof the glucose-induced localized change, either using a straight peakintensity measurement or using an integrated intensity measurement whereeach region integrated corresponds to a localized region identified inthe second-derivative plot. Alternatively, the scattering profile can berelated to blood glucose levels using a change in full width athalf-maximum measurement of one or more of the localized regionsidentified in the second-derivative plot. In addition, the scatteringprofile can be related to blood glucose levels using an anglecomputation, where the angle corresponds to a peak change in a localizedregion and an arbitrary depth.

Another aspect of the embodiment of the present invention includesidentifying localized regions of change in the scattering profile byutilizing an intensity difference plot (IDP), which is described indetail in U.S. Provisional Application No. 60/671,285. Although an IDPrequires a significant change in glucose concentrations, such as, forexample, the change caused by the subject ingesting food during thecourse of the testing time period while OCT scans are taken, one or morelocalized regions in the data curve that correspond to tissue structureswhere noticeable changes to the scattering profile were produced bychanges in blood glucose levels can be identified. Once the localizedregions are identified, the scattering profile from the localizedregions can be related to blood glucose levels using the algorithmsmentioned above.

Yet another aspect of the embodiment of the present invention includesusing a multiple-wavelength method to identify localized regions of thescattering profile that correspond to tissue and/or tissue structures,such as blood, blood vessels, or other tissue, where changes in thescattering profile due to presence of one or more analytes, such asblood glucose levels, are detectable. The term “wavelength” is usedherein to define a region of the electromagnetic radiation spectrum thatis distinguishable from other regions. While laser sources with narrowlinewidths can be preferable, other lower resolution, or even broadbandlight sources, can also be used. For example, the invention can bepracticed with two wavelengths of light, one of which might be amultimode source spanning several nanometers, e.g., 1308-1312 nm or1438-1442 nm.

As disclosed in U.S. application Ser. No. 10/916,236, the OCT-basedmonitor can be constructed such that multiple wavelengths of light areemployed to illuminate the skin. Light from multiple wavelengths isabsorbed differently by different biological constituents, whichdifferentially reduces the intensity of the scattered light. Moreover,light reflected in and around tissue can be partially absorbed by aconstituent for that wavelength. The constituent, in or around thetissue, for that wavelength absorbs some of the light according to thespecific wavelength and/or the analyte level in or around the tissue.The differences in the scattering and absorption properties produced bymultiple wavelengths interacting with different constituents provide fora determination of an optimal correlation between the scattered signaland a chosen analyte level. For example, light reflection and absorptionin and around particular tissues and tissue structures can be correlatedwith the presence of glucose to provide a measurement of blood glucoselevels. Potential tissues and tissue structures, whose light interactioncan be correlated with blood glucose levels, include (but are notlimited to) vascular tissue (e.g., blood vessel walls), blood and itscomponents (e.g., cells), dermal tissue surround blood vessels, and anycombination of the aforementioned tissues and/or tissue structures.

The wavelengths can be chosen to provide an optimal contrast between theabsorption and scattering effects of blood and other biologicalconstituents, such as water. For instance, the wavelengths can be chosento accentuate contrast regarding the presence of a particular analytethat is a signature of the presence of a tissue or tissue structuredesired to be targeted by OCT measurements (e.g., water being asignature of the presence of blood perfused tissue). A first wavelengthof light emitted from the OCT-based monitor can be chosen such thatthere is minimum absorption of the light by water compared to thescattering effect, which makes the absorption effects corresponding towater negligible, i.e., the total attenuation coefficient (μ_(s)+μ_(a))is dominated by the scattering coefficient contribution. In general,when one of the coefficients dominates another in the total attenuationcoefficient, we can assume that the less dominant coefficient can beignored. For example, we can say that μ_(s)>>μ_(a) when the scatteringcoefficient is at least about 5 times, or at least about 10 times,greater than the absorption coefficient. If a second wavelength ischosen to provide peak absorption of light by water, then the differencein light attenuation between the two wavelengths can be used to indicatethe position in depth of a blood perfused tissue structure, such as ablood vessel. Clearly, three or more wavelengths of light can also beused to generate corresponding OCT profiles, with specific wavelengthpairs utilized in a combination to generate a corresponding lightattenuation difference.

According to this aspect of the embodiment of the present invention, OCTscans are taken at two different wavelengths of light, where the firstwavelength is chosen such that the scattering effects are dominant overabsorption effects of water, and the second wavelength is chosen suchthat there is substantial absorption by water. Preferably, thescattering data sets produced by scanning a two-dimensional area of theskin by the first and second wavelengths are normalized by finding thepeak data point in each scattering data set and dividing all data pointsby the respective peak data point. Thus, each normalized scattering dataset is now a set of decimal values with each peak data point having avalue of 1.0.

The normalized scattering data set of the second wavelength can besubtracted from the normalized scattering data set of the firstwavelength to produce a differential scattering data set over the depthof the OCT signal, for a specific point in time. As discussed in U.S.Provisional Application No. 60/671,285, there are two variables orparameters associated with fitting the OCT data to blood glucose levelsin order to achieve the best correlation. These variables are an offsetand an interval. An “offset” is the depth of the OCT data curve at whichto begin correlating the OCT data to the blood glucose levels. An“interval” is a certain portion or segment of the OCT data curve that ismeasured from the offset. For each OCT data curve there are numerouspotential combinations or pairs of offsets and intervals. By identifyinga peak value in a differential scattering data set, an offset (depth)can be obtained that corresponds to tissue structures whereglucose-induced changes are dominant. If a linear fit from the peakvalue to another data point of the differential data curve is generated,the linear fit corresponds to an offset and interval combination wherethe slope of the offset and interval combination is highly correlated toblood glucose levels, i.e., the offset and interval can define thelocalized region of an OCT data curve in which an appropriateattenuation coefficient can be identified and correlated with a bloodglucose level. Thus, each linear fit can identify a localized regionwhere glucose-induced changes to the scattering profile are predominant.In cases where the OCT data curve is generated using a wavelength oflight in which μ_(s)>>μ_(a), the attenuation coefficient reduces to ascattering coefficient.

As mentioned above, by focusing on these highly localized regions alongthe scattering profile, structures within the skin can be identifiedwhere changes to the scattering profile induced by glucose are high,which allows effects induced by temperature, hydration, and otherosmolytes to be ignored.

This method focuses on the relative depths of certain structures withinthe skin, namely, capillaries where blood vessels are prevalent, andidentifies the regions of the depth scan that correlate to thesestructures where glucose levels are known to fluctuate significantly. Byfocusing on these highly localized regions of a scattering profile,glucose-induced changes to the scattering coefficient of about 2% toabout 20% per 18 mg/dL can be obtained, which is significantly higherthan the 0.2%-0.3% obtained using other noninvasive optical scatteringmethods.

A method for measuring blood glucose levels noninvasively is summarizedin the flow chart presented in FIG. 1. According to an embodiment of thepresent invention, at step S101, a non-imaging OCT-based monitor, or a“sensing” OCT-based monitor, can be utilized to take multiple scatteringcross-sectional depth measurements on an area of skin. The OCT-basedmonitor continuously scans a two-dimensional area of skin, preferablyscanning either a circle, a filled disk, or a filled rectangularpattern, where the filled pattern is drawn randomly. As the OCT-basedmonitor scans the skin, the monitor continuously collectscross-sectional depth measurements. As discussed above, continuouslyscanning a two-dimensional area of skin while continuously collectingdata reduces speckle faster than previously known methods that use anOCT-based monitor. Additionally, fewer scans are required to average outspeckle and thus, less time is required to take the scans.

At step S102, the cross-sectional depth measurements can be utilized tocreate a scattering profile in which the OCT data curve is plotted overtime. FIG. 2 shows a scattering profile of light scattered from humanskin as measured via an OCT-based monitor, according to an embodiment ofthe present invention. If an appropriate wavelength of light is chosen(e.g., around 1300 nanometers) where “an appropriate wavelength oflight” is one in which the absorption coefficient of the light, μ_(a),is small relative to the scattering coefficient, μ_(s), of the light bythe skin. A change in the OCT signal (e.g., a change in the slope of aportion of an OCT profile) likely will be dominated by glucose-inducedchanges in the tissue scattering. Based on the wavelength of lightchosen, the OCT data curve spikes at certain regions of the surface ofthe skin and then falls dramatically within the epidermis region of theskin. The OCT data curve also rises and slowly decreases within thedermis region as the depth of light in the skin increases. As shown inFIG. 2, the slope of the OCT data curve can increase or decreaserelative to the blood glucose level. That is, the slope of the OCT datacurve will change in response to glucose level changes in very smalldefined regions. Because most blood vessels are located in the dermisregion, it is this portion of the OCT data curve that provides data formeasuring blood glucose levels. To identify this region, one or more ofthe graphs described below can be generated.

At step S103, an intensity difference plot (IDP) can be generated tohighlight one or more regions of the OCT data curve that correspond totissue structures where glucose-induced changes are dominant. An exampleof an intensity difference plot is illustrated in FIG. 3. As describedin U.S. Provisional Application 60/671,285, two OCT scans are selectedand the difference in the OCT data between the selected two OCT scans iscomputed. The differential data can then be plotted to produce an IDP,as shown in FIG. 3. From the IDP, one or more zero-crossing points canbe identified as well as localized extrema surrounding the zero-crossingpoints, respectively. The IDP in FIG. 3 has one zero-crossing point,which is located at a depth of about 225 microns. A local maximum datapoint is located at around 200 microns and a local minimum point islocated at around 350 microns. The region of the localized extremarepresents a highly localized region where glucose-induced changes tothe scattering coefficient are the dominant effect within a tissuestructure, and is represented in FIG. 3 by a shaded box. To relate theOCT data to blood glucose levels, the highly localized region can befocused upon and data falling outside this region can be ignored. Withinthis region, effects due to temperature, hydration, and other osmolytesare negligible. Optionally, the box can be expanded to include potentialoffsets within a variance amount of the localized extrema. For example,in FIG. 3, the range of potential offsets includes offsets from 175microns to 400 microns.

According to another aspect of the embodiment, at step S103, thescattering profile can be used to generate a second-derivative plot. Asdescribed in U.S. Provisional Application 60/671,285, discontinuities inthe scattering profile represent structures in the skin where changesdue to variations in blood-glucose levels are high relative to changesin the scattering profile induced by other analytes. Thesecond-derivative plot enhances these discontinuities to help identifyone or more highly localized regions where the scattering profile can berelated to blood glucose levels.

FIGS. 4A and 4B graphically illustrate how a second-derivative plotenhances discontinuities in the scattering profile. In FIG. 4A, ascattering profile is plotted against the depth of the scanned area ofskin. Discontinuities in the scattering profile are identified bycircles in the graph, however, these discontinuities typically aredifficult to visualize. In FIG. 4B, a square of a second derivative ofthe scattering profile is plotted against the depth of the scanned areaof skin. The discontinuities in the scattering profile are enhanced bythe second derivative computation, while calculating the square value ofthe second derivative removes any negative values that can exist. Thediscontinuities correspond to structures in the skin where changes inblood glucose levels are dominant, such as, for example, blood vessels.The scattering data corresponding to the identified localized regionscan then be related to blood glucose levels.

Another aspect of the embodiment includes utilizing multiple wavelengthsto identify tissue and/or tissue structures with a high degree ofhydration or water content due to blood perfusion, such as blood vesselswhere changes in blood glucose levels are prevalent, at step S103. Thelocalized regions of the scattering profile that correspond to thesetissue structures then correlate well to blood glucose levels. Asdescribed above, the OCT-based monitor can utilize multiple wavelengthsof light, where one wavelength is chosen that produces a minimumabsorption of light by water in the interstitial fluid, and anotherwavelength is chosen that provides a substantial absorption of light bywater. FIG. 5 illustrates the absorption of light by water at differentwavelengths. For example, if a first wavelength of light at 1310nanometers (nm), where the absorption effects of water are minimal, anda second wavelength of light at 1440 nanometers (nm), where theabsorption effects of water are maximized, are chosen, the differentialscattering data set produced from the OCT data of the two wavelengthscan be used to determine tissue structures where hydration is high, suchas a blood vessel. Of course, other analytes indicative of a tissue ortissue structure can also be detected by the choice of appropriate lightwavelengths. For example, hemoglobin has a peak absorption at 660 nmwhen deoxygenated and 940 nm when oxygenated. Accordingly, either ofthese wavelengths can be useful to detect oxygen levels in tissue. Itcan also be advantageous to select light wavelengths such that thescattering due to the presence of a measured analyte (e.g., bloodglucose or hemoglobin) does not differ a great deal in the twowavelengths, i.e., the difference in intensity of the two wavelengths isdue mostly to the presence of water or some other analyte indicative ofthe presence of a blood vessel or other tissue structure.

The scattering profiles for each wavelength at a particular point intime can be plotted, as shown in FIGS. 6A and 6B, which representexemplary scattering profiles for first and second wavelengths of 1310nm and 1440 nm, respectively. In both FIGS. 6A and 6B, the scatteringdata set for each wavelength has been normalized using the respectivepeak intensity value. Thus, the peak intensity value for each scatteringdata set is 1.0, and each data point around the peak is less than 1.0.Because the sensitivity of the OCT-based monitor is different at the twowavelengths, the scattering profiles of the two wavelengths can not becompared directly. Normalization of the scattering data sets allowsdirect comparison of the scattering data sets from the two wavelengths.

Upon normalizing the data, the normalized scattering data set of thesecond wavelength can be subtracted from the normalized scattering dataset of the first wavelength to produce a differential scattering dataset. Using the exemplary wavelengths of 1310 nm and 1440 nm, adifferential data curve plot can be produced, as shown in FIG. 7. Theprofile of the differential data curve suggests one or more offset andinterval pairs that correspond to localized regions of the scatteringprofile where variations in blood glucose levels are the predominanteffect. One or more peak data points identified in the differential datacurve suggests one or more depths or offsets at which to begincorrelating the OCT data to blood glucose levels. Using the peak datapoint(s), one or more intervals can be identified by choosing one ormore data points on either side of the peak data point(s). Thecombination of the offset(s) and the one or more intervals producesoffset and interval pairs that can be applied to the scattering profileproduced by the first wavelength, e.g., 1310 nm, to identify localizedregions where glucose-induced effects to the scattering profile arepredominant.

Upon identifying one or more highly localized regions of the scatteringprofile where glucose-induced changes are prominent, one or morealgorithms can be used to relate the scattering profile to blood glucoselevels, at step S104. At step S104 a, the slope of portions or segmentsof the IDP data curve that correspond to the localized regions can beused to compute predicted blood glucose levels, as described in U.S.Provisional Application 60/671,285. Alternately, at step S104 b, thescattering profile can be related to blood glucose levels using amagnitude value of a localized change, either using a straight peakintensity measurement or an integrated intensity measurement using theentire localized region. Another option is to use a change in thefull-width at half-maximum measurement of one or more of the localizedregions, at step S104 c. Yet another option is to use an anglemeasurement calculation in relating the OCT data to blood glucoselevels.

The description of using multiple wavelengths to locate tissue or tissuestructures for glucose monitoring is not intended to limit the use ofthe technique to the particular application exemplified in thedescription. Indeed, beyond identifying the presence of water orhydration content of blood vessels, other analytes such as hemoglobin atvarying oxygen content can also be utilized as a signature of aparticular tissue or tissue structure (e.g., oxygenated tissue). Aswell, the types of tissue and tissue structures to which multiplewavelength OCT measurements can be used are not limited to blood vesselsbut can include other vascular tissue, blood (or particular constituentsthereof such as cells), dermal tissue surround vascular tissue, andcombinations of such exemplary tissues and tissue structures.

Furthermore, the technique is not limited to detecting blood glucose,but can be used to diagnose other conditions unrelated to blood glucose.In one instance, the technique of using of multiple wavelengths todetermine tissue hydration levels can be applied in a variety ofcontexts including assessment and/or monitoring of congestive heartfailure, management of fluid therapy for shock or surgery, management offluid load in dialysis patients (e.g., peritoneal dialysis orhemodialysis), and management of tissue hydration in pulmonary diseaseand hypertension. For example, multiple wavelength OCT measurements canbe used to monitoring clotting factors in blood. Since the scatteringcoefficient of blood is affected by hydration, use of the multiplewavelengths allows one to determine the contribution to the scatteringcoefficient that is substantially hydration independent by comparingscattering coefficients at wavelengths that absorb water strongly andweakly. The scattering coefficient at low water absorbing wavelengthscan be related to the viscosity, and eventually the clotting factors ofthe blood. Such a measurement could be useful in post-surgicalmonitoring of patients who are administered blood thinning agents. Thescattering coefficient at low water absorbing wavelengths can also beadjusted using the measurements at higher water absorbing wavelengths.When using any of the calibration methods encompassed by the presentapplication, actual samples of the measured analyte (or othernon-chemically oriented types of analyte measurements) can be utilizedto aid in calibration (e.g., the use of blood glucose samples asdescribed with reference to glucose monitoring herein).

In a further aspect of the embodiment, multiple wavelength OCTmeasurements can be utilized to provide an improved estimate of ascattering coefficient or an absorption coefficient from tissuemeasurements. Such an aspect can be utilized in conjunction with any ofthe potential applications of the present invention such as determiningthe viscosity of blood. The following description is with reference toestimating an absorption coefficient, though estimates of a scatteringcoefficient can also be obtained under analogously consistentconditions.

In one example, a pair of OCT scattering profiles are obtained, eachprofile corresponding to a measurement at a particular wavelength oflight. With reference to the S101 and S102 of the flowchart of FIG. 1and the corresponding description, the profiles can be obtained byscanning a two-dimensional area of skin to obtain measurements at anumber of cross-sectional depths. In this particular example, oneprofile is obtained using light with a wavelength of about 1310 nm andanother profile is obtained using 1440 nm light. The intensity of thereflected light at 1310 nm can be approximated by the followingequation:

${\ln( \frac{I_{R}}{I_{O}} )}^{1310} = {- {L\lbrack {\mu_{s}^{1310} + \mu_{a}^{1310}} \rbrack}}$where I_(R) is the reflected light intensity at 1310 nm, I_(o) is theinitial light intensity at 1310 nm, L is the total light pathlength,μ_(s) ¹³¹⁰ is the scattering coefficient of the tissue at 1310 nm, andμ_(a) ¹³¹⁰ is the absorption coefficient of the tissue at 1310 nm.

In some instances, a wavelength can be selected such that one of thescattering or absorption coefficients is stronger than the other to theextent that the contribution of the weaker can be ignored (e.g., whenone contribution is at least about 5 times greater or at least about 10times greater than the other). When measuring hydration levels, thescattering coefficient μ_(s) ¹³¹⁰ is stronger than the absorptioncoefficient μ_(a) ¹³¹⁰ such that the contribution from μ_(a) ¹³¹⁰ can beignored; this allows the scattering coefficient μ_(s) ¹³¹⁰ to bedetermined. Accordingly, a plot of ln(I_(R)/I_(o)) versus depth canyield a line with a slope that can be equated with μ_(a) ¹³¹⁰.

The scattering coefficient at 1310 nm μ_(s) ¹³¹⁰ can be used to providea measure of the scattering coefficient at 1440 nm, μ_(s) ¹⁴⁴⁰. Variousscattering theories, as known to those skilled in the art, can be usedto relate the scattering coefficients at the two different wavelengths.For example, under Mie scattering, (0.7) μ_(s) ¹³¹⁰≈μ_(s) ¹⁴⁴⁰. Usingthis estimate for μ_(s) ¹⁴⁴⁰, an estimate of the absorption coefficientat 1440 nm can be found using:

${\ln( \frac{I_{R}}{I_{o}} )}^{1440} = {- {L\lbrack {\mu_{s}^{1440} + \mu_{a}^{1440}} \rbrack}}$where I_(R) is the reflected light intensity at 1440 nm, I_(o) is theinitial light intensity at 1440 nm, L is the total light path length,μ_(s) ¹⁴⁴⁰ is the scattering coefficient of the tissue at 1440 nm, andμ_(a) ¹⁴⁴⁰ is the absorption coefficient of the tissue at 1440 nm. TheOCT profile at 1440 nm, along with the estimated scattering coefficientμ_(s) ¹⁴⁴⁰, can allow one to determine μ_(a) ¹⁴⁴⁰.

As previously mentioned, the outlined technique can also be used todetermine scattering coefficients when a scattering profile utilizes awavelength in which an absorption coefficient dominates (e.g., anabsorption coefficient is measured using a wavelength where absorptiondominates attenuation, followed by estimating an absorption coefficientat a second wavelength and determining the scattering coefficient at thesecond wavelength). Those skilled in the art will appreciate that thetechnique can also be applied with respect to other analytes besideswater (e.g., hemoglobin) when appropriate wavelengths of light arechosen.

As discussed in U.S. application Ser. No. 10/916,236, the use ofmultiple wavelengths can also provide an additional sensor calibrationtechnique. Using water detection as an exemplification of calibratinganalyte effects on OCT measurements, the scattering coefficient of afirst wavelength OCT measurement can drift even though the glucoseconcentration remains static because of the change in the scatteringcoefficient due to hydration changes. Thus, by measuring the skinhydration using a second wavelength in which the wavelength is selectedsuch that the resulting scattering profile tracks hydration changes(e.g., the absorption coefficient at the second wavelength is high forwater, and much higher relative to the absorption coefficient at thefirst wavelength), this drift can be compensated for and the OCT sensorcan maintain calibration. Clearly, other analytes that can effectscattering coefficient measurements can also be compensated for usingthis technique.

FIG. 8 is a schematic block diagram of a method of measuring the bloodglucose concentration on a human or animal subject. The first step,shown in Block A, is to provide light having scattering absorption orproperties sensitive to glucose concentration within the tissue.Preferably the light provided comprises at least two differentwavelengths. By different wavelengths is meant that the wavelengthsshould be sufficiently different that they have measurably differentabsorption and scatter properties for different levels of glucose and/orindicator components such as blood. Typically, the light is providedfrom multiple single wavelength sources, such as low coherencesuperluminescent diodes (SLEDs) at wavelengths in the red/near infraredrange (RNIR). Alternatively, the light can be provided from a singlebroadband source appropriately notch filtered. Both wavelengths of lightare advantageously directed in a single beam.

The next step, shown in Block B, is to split the single beam of lightinto a reference beam and a sample beam. The reference beam travels inan adjustable phase path denoted as the reference beam path (referencearm), and the sample beam travels in a sample beam path (sample arm)where it is directed onto the tissue to be monitored, e.g. the skin of ahuman diabetic. The light in the reference beam is directed over anadjustable phase path and will subsequently be interfered with samplelight reflected from within the tissue.

In the third step, Block C, the sample beam is continuously or nearcontinuously scanned over a two-dimensional area of the tissue while, atthe same time, being interferometrically scanned in depth. Block D showsvarying the phase (path length) of the reference beam so that light fromthe reference beam constructively interferes with reflected sample lightfrom successively different depths of tissue. Block E shows thereflected light collected and interfered with the reference beam. As theinterferometer sweeps in depth, the surface scan is also sweepingcontinuously. This “smears” out the scan and reduces the effect ofspeckle.

The next steps, Blocks F, G, and H are to process the resulting data tocalculate glucose concentration. In essence, this is achieved bycomputing the scattering coefficient of glucose-containing tissue. BlockF indicates the scanning data is input into a digital processor. BlockG, which is optional, but advantageous, is to identify those scatteringmeasurements that are from blood-perfused tissue (in or near bloodvessels). Such identification can be accomplished, for example, byproviding light of two different wavelengths, at least one of whichscatters from blood perfused tissues in a characteristic manner.Finally, in Block H, the scattering coefficient of the glucosecontaining tissue is calculated, and the correlated glucose level inblood is determined.

FIG. 9 schematically shows advantageous apparatus 900 for practicing themethod of FIG. 8. The apparatus 900 comprises a fiber optics based lowcoherence interferometer (LCI). A 2×2 fiber optic splitter 901 forms thebasic interferometer. An optical input from light sources 906 is splitbetween a sample beam 902 and a reference beam 904. Sample light in beam902 is continuously scanned across a sample surface by scanner 908.Preferably, the end of the sample beam 902 can contain imaging optics903 to tailor the spot size according to the area tissue being measured.Reference beam 904 is varied or adjusted in phase as by a moveable minor905 which can be vibrated or oscillated to scan depth. Reflected signalsfrom beams 902 and 904 interfere and are presented to photodetector 907for measurement. Advantageously, imaging optics 903 can provide highcoupling efficiency between the optical system and the tissue.

The tissue volume with which the light interacts (referred to as theinteraction volume) is determined by the spot size of the imaging optics(surface area) and the coherence length of the light (depth). Thereference beam 904 has a scanning reflector 905 (such as a mirror). Thereflector 905 of the interferometer determines the phase shift appliedto the reference beam 904 and thus, which reflected light from thereference beam 904 will constructively interfere with the reflectedsample beam 903. The differences in phase of the beams determines thedepth from which scattered light will be measured. This can permit afixed depth, adjustable depth, or a scan of multiple depths within thetissue. LCI is thus sensitive to the intensity of the reflected lightlocalized in a small volume of tissue. Determination of the depth andinteraction volume permits a more accurate selection of regions ofblood-perfused tissue beneath the skin.

A photodetector 907 (such as a photodiode) can be used to measure theinterference of the light from both the sample beam 902 and thereference beam 904. One or more photodetectors 907 may be used alongwith optical filters (not shown) designed for each of the differentwavelength light sources 906 used in the measurement.

Preferably, the imaging optics 903 are beam focusing optics to reducethe beam cross section so as to minimize the region of opticalinteraction with the tissue. The use of these optics will enhance theselectivity of the signal while also reducing the effect of speckle.

Light passing through turbid biological tissue is subject to wavefrontdistortion that produces coherent noise or “speckle”. The effect ofspeckle can be reduced by taking multiple scans from different locationson the tissue and then averaging these scans. This solution isimpractical for the typical OCT imaging system, because the vast numberof scans needed to reduce speckle would take too long and would producea severe loss in the resolution of the image. However, for the presentdisclosure, the collection optics can be simpler. The presentnon-imaging system presents a practical solution to reducing coherentnoise. Not only does the speckle effect significantly decrease, but thenon-imaging system can continuously scan a two-dimensional area oftissue instead of being limited to a single scanning line. Area scansreduce speckle due to the diversity of tissue regions encompassed in thescan. They also maximize the coverage of blood-perfused tissue. Thus,coherent noise is also further reduced.

An alternative solution is to use parallel optical processing wheremultiple spots on the subject tissue are measured together to create“boiling” speckle. Boiling speckle occurs where the sub-spot speckle ischanging so quickly that the observed speckle is averaged out by thehuman eye, or the integration time of the optical receiver. Thisinventive system may be modified to create boiling speckle by replacingthe scanner 908 with either a lenslet array or a diffractive opticalelement (DOE). If the lenslet or DOE is rapidly translated or rotatedabout the optical axis at an very high speed, the observed speckle willbe averaged out. Additionally, such a system reduces the number of scansrequired due to the greater variety of speckle detected.

Since glucose is delivered to the interstitial fluid (IF) in skin viablood, determining the scatter coefficient in the dermis layer of thetissue, where blood vessels are plentiful, provides the closestcorrelation to variations in glucose concentration. Again, an area scanincreases the volume of blood-perfused tissue measured.

Area scanning could be achieved by a pair of rotating prisms thatcontinuously move a sample beam spot over a circular area of the tissuesurface. Advantageously, the spot would move a minimum of one spotdiameter for each depth scan. Thus if the beam spot size is 12 micronsand the depth scan is at a rate of 20 Hz, then the spot shouldadvantageously be moved at a minimum rate of 240 microns per second andpreferably much faster.

Spot diameters are typically in the range from about 10 microns to 100microns and preferably 20 microns and higher.

The minimum area of the scan is defined by the number of spot diametersneeded to move at the minimum depth scan rate. For the 12 micron spotand 20 Hz depth scan, the minimum area that would need scanning is about2200 square microns, corresponding to a circular area of about 500micron diameter. More preferably the system would be designed to coveran area corresponding to a diameter of 500 microns to 10,000 microns.

For speckle reduction using the boiling speckle method of noisereduction, the multiple spots would need to be moved quite rapidly. Thespot should move at a minimum of one spot diameter in the integrationtime of the receiver. For individual spot sizes of about 10 microns andan integration time of about 4 microseconds, the spots would need tomove at a minimum of 2.4×10⁵ microns/sec.

The light sources 906 can be light emitting diodes (LED) or superluminescent diodes (SLEDs), both of which are semiconductor based lightemitters whose wavelengths can be chosen to give the best contrastbetween absorption and scatter of blood and other biologicalconstituents, such as water. Typically these wavelengths are in thered/near infrared (RNIR) region of the spectrum, however, longer andshorter wavelengths can be used for enhanced sensitivity. For theglucose measurements, two or more light sources are advantageous and canshare the same optical paths through the interferometer.

One of the wavelengths can be chosen to have minimum absorption comparedto the scattering coefficient for water and blood constituents. If theother wavelength is chosen to have peak absorption for certainbiological constituents, then the difference in light attenuationbetween the two wavelengths can indicate the position in depth of arelevant structure, such as a blood vessel. Light from the twowavelengths is differently absorbed by the different constituents. Thisdifferential absorption differentially reduces the intensity of thescattered (reflected) light. Light reflected off the cellular membraneis partially absorbed by the respective constituent for that wavelength.Where the term “light is reflected from the blood” is used, it isunderstood to refer to light reflected from the cells in and around theblood vessels, and the constituent in the blood absorbs some of thelight according to the specific wavelength and glucose level of theblood. These differences in the scattering and absorption propertiesprovide for an optimal correlation between the scattered signal andblood glucose data.

One exemplary application is a first wavelength of about 1310 nm and asecond wavelength of about 820-960 nm. A first wavelength of 1310 nm ischosen because the scattering properties of water and blood and bloodconstituents is at a maximum compared to the absorption properties ofthese fluids. The second wavelength, 820-960 nm, is chosen because theabsorption of light is very high in the presence of hemoglobin, a bloodconstituent, (compared to the first wavelength). If the signal of thesecond wavelength were to experience a rapid decrease at a particulardepth in the interaction volume, this rapid decrease would indicate thepresence of hemoglobin, and hence, the location of blood-perfusedtissue. It would thus indicate an optimal slope region for thescattering data of the first wavelength to be related to the glucoseconcentration.

A second example would be a first wavelength of about 1310 nm and asecond wavelength of about 1450 nm. At this second wavelength, thescattering coefficients for blood and water are similar to those of thefirst wavelength. However, the absorption coefficient for water at thissecond wavelength is exponentially larger than that of the firstwavelength. Thus, a differential measurement between these twowavelengths indicates changes in the hydration level of the tissue. Suchchanges can then be used to indicate an optimal slope region formeasuring blood glucose. However, the use of these two specificwavelengths provides an additional benefit of sensor calibration. As thehydration level in the dermis layer varies, the scattering coefficientof the first wavelength may drift, even though the glucose concentrationremains static. Thus, by measuring the skin hydration using the secondwavelength, this drift can be compensated for and the OCT sensor canmaintain calibration.

While the present invention has been described with respect toparticular embodiments discussed herein, it is to be understood that theinvention is not limited to the disclosed embodiments. To the contrary,the invention is intended to cover various modifications and equivalentarrangements included within the spirit and scope of the appendedclaims. The scope of the following claims is to be accorded the broadestinterpretation so as to encompass all such modifications and equivalentstructures and functions.

What is claimed is:
 1. A physiological monitoring system for determininga hemoglobin level using an optical sensor, the system comprising: anon-invasive reflective optical sensor configured to irradiatebiological tissue using a beam of light; and a physiological monitorcomprising at least one processor, the physiological monitor configuredto: obtain scattering measurements of light from the beam reflected bythe biological tissue using Optical Coherence Tomography; identifyportions of the measurements of the light corresponding to measurementstaken within one or more blood vessels within the biological tissue; andestimate the hemoglobin level based at least partly on the measurementstaken within the one or more blood vessels.
 2. The system of claim 1,wherein the beam comprises at least two different wavelengths havingmeasurably different scattering properties for hemoglobin-containingtissue.
 3. The system of claim 2, wherein the physiological monitor isconfigured to estimate the hemoglobin level by identifyinghemoglobin-containing tissue based at least partly on the differentscattering properties of the at least two different wavelengths of thebeam.
 4. The system of claim 1, wherein the physiological monitor isconfigured to identify the portions of the measurements by: generating afirst scattering profile; identifying a localized region in the firstscattering profile where measurement changes due to blood hemoglobinpredominate; and selecting portions of the measurements of the lightcorresponding to the identified localized region.
 5. The system of claim4, wherein the physiological monitor is further configured to: generatea second scattering profile; and compare the second scattering profilewith the first scattering profile to identify a region where measurementchanges due to blood hemoglobin predominate.
 6. The system of claim 1,wherein the beam comprises at least one of infrared and near infraredlight.
 7. The system of claim 1, wherein the non-invasive reflectiveoptical sensor is configured to generate a reference beam that isadjusted to constructively interfere with the light reflected by thebiological tissue.
 8. The system of claim 1, wherein the measurements oflight include light reflected from hemoglobin in the biological tissue.9. A physiological monitoring system for determining a hemoglobin levelusing an optical sensor, the system comprising: a physiological monitorcomprising at least one processor, the physiological monitor configuredto: obtain measurements of reflected light detected by a non-invasivereflective optical sensor from a beam of light generated by thereflective sensor and reflected from biological tissue, the beam oflight comprising two or more wavelengths selected to differentiatehemoglobin from another biological constituent based on scatteringproperties of the beam; generate light scatter measurements based atleast partly on the measurements of the reflected light from the beam oflight; process the light scatter measurements to determine thehemoglobin level of the biological tissue based at least partly onidentifying hemoglobin from the light scatter measurements.
 10. Thesystem of claim 9, wherein the physiological monitor is configured toprocess the light scatter measurements by identifying the hemoglobinbased at least partly on the scattering properties of the beam.
 11. Thesystem of claim 9, wherein the non-invasive reflective optical sensor isconfigured to generate a reference beam that is adjusted toconstructively interfere with the reflected light from the illuminatedbiological tissue.
 12. The system of claim 9, wherein the measurementsof reflected light include light reflected from hemoglobin in thebiological tissue.
 13. The system of claim 9, wherein the two or morewavelengths each have different scattering properties forhemoglobin-containing tissues from the other.
 14. The system of claim13, wherein the at least two wavelengths comprise wavelengths at about1310 nm, and either about 1450 nm or about 820-960 nm.
 15. The system ofclaim 13, wherein at least one of the two or more wavelengths of lightis infrared or near infrared light.
 16. The system of claim 13, whereindifferences in the scattering properties produced by the at least twowavelengths indicates the presence of hemoglobin.
 17. A method ofmeasuring hemoglobin concentration within biological tissue, the methodcomprising: generating a beam of light, the beam comprising two or morewavelengths selected to differentiate hemoglobin from another biologicalconstituent based on scattering properties of the beam; and illuminatingbiological tissue with the beam; generating light scatter measurementsbased at least partly on light from the beam reflected by theilluminated biological tissue; and processing the light scattermeasurements to determine a hemoglobin level of the biological tissuebased at least partly on identifying hemoglobin based on the scatteringproperties of the beam.
 18. The method of claim 17, further comprisinggenerating a reference beam and interfering said reference beam with thelight from the beam to generate light scatter measurements.
 19. Themethod of claim 17, wherein the two or more wavelengths each havedifferent scattering properties for hemoglobin-containing tissues fromthe other.
 20. The method of claim 19, wherein the at least twowavelengths comprise wavelengths at about 1310 nm, and either about 1450nm or about 820-960 nm.
 21. The method of claim 19, wherein at least oneof the two wavelengths of light is infrared or near infrared light. 22.The method of claim 19, wherein differences in the scattering propertiesproduced by the at least two wavelengths indicates the presence ofhemoglobin.
 23. The method of claim 19, wherein the at least twowavelengths are selected to accentuate contrast between hemoglobin andone or more other biological constituents.
 24. The method of claim 17,wherein the beam comprises at least three wavelengths, each havingdifferent scattering properties for hemoglobin-containing tissues fromthe other.
 25. The method of claim 17, wherein the hemoglobin level isestimated based at least partly on identifying hemoglobin using lightscatter measurements corresponding to one or more blood vessels.
 26. Themethod of claim 23, wherein the one or more other biologicalconstituents are located around the hemoglobin.
 27. The method of claim17, wherein the light from the beam reflected by the illuminatedbiological tissue includes light reflected from hemoglobin in theilluminated biological tissue.